Design of a Computational Framework for Predicting Cancer Susceptibility Using Genomic Data: A Case Study of Bayero University, Kano State
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- NGN 5000
Background of the Study :
Cancer remains one of the leading causes of death worldwide, and early prediction of cancer susceptibility is crucial for improving patient outcomes. Recent advances in genomic technologies and computational methods have opened new avenues for the development of predictive frameworks. This study focuses on designing a computational framework that leverages genomic data to predict cancer susceptibility. By integrating high-throughput sequencing data with advanced machine learning algorithms, the proposed framework aims to identify genetic markers and mutations that predispose individuals to various cancer types. At Bayero University, Kano State, the availability of local genomic datasets provides a unique opportunity to tailor predictive models to the genetic diversity observed in the population (Okeke, 2023). The framework will incorporate data preprocessing techniques, variant calling, and annotation pipelines to ensure high-quality input for the prediction models. Moreover, the use of ensemble learning and deep neural networks is expected to enhance the sensitivity and specificity of cancer risk prediction (Aminu, 2024). The study emphasizes the importance of integrating clinical data with genomic profiles to improve model interpretability and facilitate personalized risk assessment. In addition, ethical considerations such as data privacy and informed consent are integral components of the framework design. Recent research has demonstrated that integrating multi-omics data can significantly improve predictive accuracy, and this study will build on those findings by focusing on genomic markers specific to Nigerian populations (Bello, 2025). The framework is designed to be modular, allowing for updates as new genetic markers are discovered, and scalable to accommodate increasing data volumes. Furthermore, the study addresses potential challenges such as data heterogeneity and computational resource limitations by incorporating cloud-based processing and parallel computing strategies. Overall, the research aims to provide a robust and user-friendly computational tool that not only predicts cancer susceptibility but also offers insights into the underlying genetic mechanisms. This approach is expected to guide early interventions and improve overall healthcare delivery in regions with limited access to advanced medical technologies.
Statement of the Problem :
Despite the significant advances in genomic research, predicting cancer susceptibility remains a complex challenge due to the multifactorial nature of the disease. Current predictive models are often limited by their reliance on datasets derived predominantly from Western populations, thereby failing to capture genetic variations unique to African cohorts. In Nigeria, the lack of comprehensive genomic databases and standardized analytical pipelines has resulted in suboptimal risk prediction models. Moreover, existing computational frameworks struggle with integrating heterogeneous data types, including genomic, clinical, and environmental factors, which are critical for accurate cancer risk assessment (Ibrahim, 2023). There is also a considerable challenge in ensuring that the predictive algorithms are both interpretable and scalable. The complexity of deep learning models, while offering high accuracy, may reduce transparency, limiting clinical adoption. Additionally, computational resource constraints in many Nigerian research institutions further hinder the development and implementation of such advanced models (Uche, 2024). Ethical issues regarding data security and patient privacy also pose significant barriers, especially when handling sensitive genomic information. This study aims to address these challenges by developing a computational framework that is specifically tailored to the Nigerian genetic landscape, integrating robust preprocessing methods and scalable machine learning techniques. By validating the framework using local genomic data from Bayero University, the research seeks to enhance the predictive performance and clinical relevance of cancer susceptibility models. The ultimate goal is to provide healthcare professionals with a reliable tool for early detection and personalized intervention strategies, thereby reducing the cancer burden in the region (Abdul, 2025).
Objectives of the Study:
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To design a computational framework that integrates genomic data for cancer susceptibility prediction.
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To evaluate the performance of machine learning algorithms in identifying genetic risk markers.
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To validate the framework using local genomic datasets from Bayero University.
Research Questions:
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How effective is the proposed computational framework in predicting cancer susceptibility?
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What are the key genetic markers identified by the model in the local population?
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How does the integration of clinical data enhance the predictive accuracy of the framework?
Significance of the Study :
This study is significant because it pioneers a computational framework tailored to the Nigerian genetic context, providing a novel approach to predicting cancer susceptibility. The integration of genomic and clinical data promises to enhance early detection and personalized treatment strategies. By addressing current gaps in data diversity and model interpretability, the research has the potential to improve clinical decision-making and reduce the cancer burden. The outcomes will inform healthcare policies and drive future research in precision medicine, particularly in resource-limited settings (Aminu, 2024).
Scope and Limitations of the Study:
The study is limited to designing and validating a computational framework for predicting cancer susceptibility using genomic data from Bayero University, Kano State. It does not extend to longitudinal clinical trials or external populations beyond the local context.
Definitions of Terms:
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Computational Framework: A structured system that integrates various computational tools and methods for data analysis.
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Genomic Data: Information derived from an individual’s DNA sequence, including gene variants and mutations.
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Cancer Susceptibility: The likelihood or predisposition of an individual to develop cancer based on genetic and environmental factors.
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